Ensemble selection for evolutionary learning using information theory and Price's theorem

Stuart W. Card, Chilukuri K. Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper presents an information theoretic perspective on design and analysis of evolutionary algorithms. Indicators of solution quality are developed and applied not only to individuals but also to ensembles, thereby ensuring information diversity. Price's Theorem is extended to show how joint indicators can drive reproductive sampling rate of potential parental pairings. Heritability of mutual information is identified as a key issue.

Original languageEnglish (US)
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery (ACM)
Pages1587-1588
Number of pages2
ISBN (Print)1595931864, 9781595931863
DOIs
StatePublished - 2006
Event8th Annual Genetic and Evolutionary Computation Conference 2006 - Seattle, WA, United States
Duration: Jul 8 2006Jul 12 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference
Volume2

Other

Other8th Annual Genetic and Evolutionary Computation Conference 2006
CountryUnited States
CitySeattle, WA
Period7/8/067/12/06

Keywords

  • Ensemble models
  • Evolutionary computation
  • Group selection
  • Machine learning
  • Mate selection
  • Price's Equation

ASJC Scopus subject areas

  • Engineering(all)

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